_base_ = "../faster_rcnn/faster_rcnn_r50_caffe_fpn_1x_coco.py"
model = dict(
    rpn_head=dict(
        _delete_=True,
        type="GARPNHead",
        in_channels=256,
        feat_channels=256,
        approx_anchor_generator=dict(
            type="AnchorGenerator",
            octave_base_scale=8,
            scales_per_octave=3,
            ratios=[0.5, 1.0, 2.0],
            strides=[4, 8, 16, 32, 64],
        ),
        square_anchor_generator=dict(
            type="AnchorGenerator", ratios=[1.0], scales=[8], strides=[4, 8, 16, 32, 64]
        ),
        anchor_coder=dict(
            type="DeltaXYWHBBoxCoder",
            target_means=[0.0, 0.0, 0.0, 0.0],
            target_stds=[0.07, 0.07, 0.14, 0.14],
        ),
        bbox_coder=dict(
            type="DeltaXYWHBBoxCoder",
            target_means=[0.0, 0.0, 0.0, 0.0],
            target_stds=[0.07, 0.07, 0.11, 0.11],
        ),
        loc_filter_thr=0.01,
        loss_loc=dict(
            type="FocalLoss", use_sigmoid=True, gamma=2.0, alpha=0.25, loss_weight=1.0
        ),
        loss_shape=dict(type="BoundedIoULoss", beta=0.2, loss_weight=1.0),
        loss_cls=dict(type="CrossEntropyLoss", use_sigmoid=True, loss_weight=1.0),
        loss_bbox=dict(type="SmoothL1Loss", beta=1.0, loss_weight=1.0),
    ),
    roi_head=dict(bbox_head=dict(bbox_coder=dict(target_stds=[0.05, 0.05, 0.1, 0.1]))),
)
# model training and testing settings
train_cfg = dict(
    rpn=dict(
        ga_assigner=dict(
            type="ApproxMaxIoUAssigner",
            pos_iou_thr=0.7,
            neg_iou_thr=0.3,
            min_pos_iou=0.3,
            ignore_iof_thr=-1,
        ),
        ga_sampler=dict(
            type="RandomSampler",
            num=256,
            pos_fraction=0.5,
            neg_pos_ub=-1,
            add_gt_as_proposals=False,
        ),
        allowed_border=-1,
        center_ratio=0.2,
        ignore_ratio=0.5,
    ),
    rpn_proposal=dict(max_num=300),
    rcnn=dict(
        assigner=dict(pos_iou_thr=0.6, neg_iou_thr=0.6, min_pos_iou=0.6),
        sampler=dict(type="RandomSampler", num=256),
    ),
)
test_cfg = dict(rpn=dict(max_num=300), rcnn=dict(score_thr=1e-3))
optimizer_config = dict(_delete_=True, grad_clip=dict(max_norm=35, norm_type=2))
